Unifying Information-Theoretic and Pair-Counting Clustering Similarity

ArXi:2511.03000v2 Announce Type: replace-cross Comparing clusterings is central to evaluating unsupervised models, yet the many existing similarity measures can produce widely divergent, sometimes contradictory, evaluations. Clustering similarity measures are typically organized into two principal families, pair-counting and information-theoretic, reflecting whether they quantify agreement through element pairs or aggregate information across full cluster contingency tables.